Optical projection imaging system and method for automatically detecting cells having nuclear and cytoplasmic densitometric features associated with disease

ABSTRACT

A system and method for rapidly detecting cells of interest using multi-dimensional, highly quantitative, nuclear and cytoplasmic densitometric features (NDFs and CDFs) includes a flow optical tomography (FOT) instrument capable of generating various optical projection images (or shadowgrams) containing accurate density information from a cell, a computer and software to analyze and reconstruct the projection images into a multi-dimensional data set, and automated feature collection and object classifiers. The system and method are particularly useful in the early detection of cancers such as lung cancer using a bronchial specimen from sputum or cheek scrapings and cervical/ovarian cancer using a cervical scraping, and the system can be used to detect rare cells in specimens including blood.

RELATED APPLICATIONS

[0001] This application is a continuation-in-part of co-pending U.S.application Ser. No. 09/927,151 of Alan C. Nelson, filed Aug. 10, 2001,that is in turn related to co-pending provisional application of Alan C.Nelson, Ser. No. 60/279244, filed Mar. 28, 2001, both entitled“APPARATUS AND METHOD FOR IMAGING SMALL OBJECTS IN A FLOW STREAM USINGOPTICAL TOMOGRAPHY.” By this reference, applicant claims the benefit ofthe priority filing date of the co-pending provisional application.

FIELD OF THE INVENTION

[0002] The present invention relates to projection imaging systems ingeneral and cell classification, and more particularly, to highthroughput flow based automated systems using projection imaging, suchas flow optical tomography (FOT), for detecting abnormal and malignantcells and for detecting rare cells based on highly quantativemeasurements of nuclear and cytoplasmic densitometric features (NDFs andCDFs) associated with disease.

BACKGROUND OF THE INVENTION

[0003] The most common method of diagnosing cancer in patients is byobtaining a sample of the suspect tissue and examining it under amicroscope for the presence of obviously malignant cells. While thisprocess is relatively easy when the anatomic location of the suspecttissue is known, it is not so easy when there is no readily identifiabletumor or pre-cancerous lesion. For example, to detect the presence oflung cancer from a sputum sample requires one or more relatively rarecancer cells to be present in the sample. Therefore, patients havinglung cancer may not be diagnosed properly if the sample does notperceptively and accurately reflect the conditions of the lung.

[0004] One example of a microscope-based system and method for detectingdiagnostic cells and cells having malignancy-associated changes isdisclosed in Palcic et al., U.S. Pat. No. 6,026,174. The Palcic et al.system includes an automated classifier having a conventionalmicroscope, camera, image digitizer, a computer system for controllingand interfacing these components, a primary classifier for initial cellclassification, and a secondary classifier for subsequent cellclassification. The method utilizes the automated classifier toautomatically detect diagnostic cells and cells havingmalignancy-associated changes. However, the quality of the diagnosticresult is limited by the use of a conventional microscope, which doesnot permit accurate measurement of stain densities. The method of Palcicet al. does not address the use of molecular probes.

[0005] With the advent of molecular probes, such as antibody probes andnucleic acid hybridization probes, new disease related questions can beaddressed by tagging these molecular probes and then measuring theirlocation and concentration within biological cells and tissues. As theneed to more accurately localize and quantify these probes is emerging,there is a concomitant need for improved techniques to measure probedensities microscopically in two dimensions (2D) and three dimensions(3D). Conventional light microscopy, which utilizes cells mounted onglass slides, can only approximate 2D and 3D measurements because oflimitations in focal plane depth, sampling angles, and problems withcell preparations that typically cause cells to overlap in the plane ofthe image. Another drawback of light microscopy is the inherentlimitation of viewing through an objective lens where only the areawithin the narrow focal plane provides accurate data for analysis.

[0006] Flow cytometry methods generally overcome the cell overlapproblem by causing cells to flow one-by-one in a fluid stream.Unfortunately, flow cytometry systems do not generate images of cells ofthe same quality as traditional light microscopy, and, in any case, theimages are not three-dimensional. For background, those skilled in theart are directed to Shapiro, HM, Practical Flow Cytometry, 3^(rd) ed.,Wiley-Liss, 1995.

[0007] In the area of computer aided tomography, U.S. Pat. No.5,402,460, issued Mar. 28, 1995, to Johnson, et al. entitled“Three-dimensional Microtomographic Analysis System” discloses amicrotomographic system for generating high-resolution,three-dimensional images of a specimen using an x-ray generator and anx-ray detector that measures the attenuation of the x-ray beam throughthe specimen. Two projections, each using a different energy x-ray beam,of each view of the specimen are made with Johnson, et al'smicrotomographic system. After the two projections of one view of thespecimen are made, the specimen is rotated on the specimen holder andanother set of projections is made. The projections of each view of thespecimen are analyzed together to provide a quantitative indication ofthe phase fraction of the material comprising the specimen. Theprojections of the different views are combined to provide athree-dimensional image of the specimen. U.S. Pat. No. 5,402,460 isincorporated herein by reference. Although the x-ray technology astaught by U.S. Pat. No. 5,402,460 is useful for some applications, itdoes not provide an optical solution useful for flow cytometry, wherebyone could measure the 3D distribution of molecular density within abiological cell.

[0008] To overcome the aforementioned limitations and others found insuch systems, it is a motivation of this invention to combine theone-by-one cell presentation of flow cytometry with computationaloptical tomography from multiple point source projections to reconstructdensity information within a cell from a plurality of projections. Thereconstructed 3D density information enables the accurate measurement ofnuclear densitometric features (NDFs) and cytoplasmic densitometricfeatures (CDFs).

[0009] The NDFs and CDFs referred to herein are unique to projectionimaging systems that do not require the use of lenses and focal planeswith their inherent unwanted blur artifact due to unfocussed structuresoutside the narrow focal plane. Because projection imaging systems donot require lenses and focal planes, but rather, produce shadowgramswherein all structures are in clear focus all at once, the measurementof density features will be more quantitative and accurate than inconventional microscopy systems. Therefore, the terms “NDF” and “CDF”refer to density feature measurements from shadowgrams and tomographicreconstructions using shadowgrams. NDFs and CDFs are subtle changes thatare known to take place in cells associated with cancer tissue. Thesechanges are indicative of deviation from normalcy and reflect molecularchanges associated with the disease process.

[0010] However, NDFs and CDFs have not yet achieved wide acceptance forscreening to determine whether a patient has or will develop cancer,because the methods of measurement have not provided adequate accuracyand/or throughput. Traditionally, NDFs and CDFs have been detected bycarefully selecting a cell sample from a location near a tumor orpre-cancerous lesion and viewing the cells under a microscope usingrelatively high magnification. However, it is believed that the NDF andCDF changes that take place in the cells may be too subtle to bereliably detected by a human pathologist working with conventionalmicroscopy equipment, especially because the pathologist is typicallynot making quantitative measurements. For example, an NDF change may beindicated by the distribution and density of DNA within the nucleuscoupled with slight variations in the shape of the nucleus. Becausehuman observers cannot easily quantify such subtle cell changes, it isdifficult to determine which cells exhibit NDF alterations.

SUMMARY OF THE INVENTION

[0011] In one embodiment, the present invention provides a method fordetecting cells of interest in a cell sample, comprising the steps ofobtaining a cell sample and suspending the cells in solution; ifrequired, fixing the cells of the cell sample in solution; stainingand/or labeling the cells to generate optical densities associated withnuclear molecules or other structures within each cell of the sample;illuminating the sample with a “point” source of light and obtaining oneor more projection images (e.g. shadowgrams) through the sample with adigital array detector; compensating the projection images forvariations in background illumination; analyzing the projection imagesto detect objects of interest; calculating a set of one-dimensional (1D)and two-dimensional (2D) feature values for each object of interest; andproviding the set of feature values to at least one classifier thatidentifies and characterizes cells of interest in the cell sample.

[0012] In another aspect, the present invention provides a system forautomatically detecting NDFs and CDFs in cell samples. The preferredsystem includes a flow optical tomography (FOT) instrument that iscontrolled by and interfaced with a computer system. Projection imagescaptured by the FOT are stored in an image processing board andmanipulated by the computer system to detect the presence ofone-dimensional and two-dimensional NDFs and CDFs. Multiple projectionimages can be reconstructed by the computer to generatethree-dimensional (3D) and higher-dimensional (3D+) images with theirassociated NDFs and CDFs.

[0013] To measure the NDFs and CDFs, a cell sample is obtained andstained in suspension, and then imaged by the FOT. The stain isstoichiometric and/or proportional to DNA and/or its associated proteinsor to other molecules of interest within the cell including thecytoplasm. The computer system then analyzes the projection imagesdirectly and/or computes the 3D reconstruction that is also analyzed.The images are corrected for uneven illumination and other imperfectionsof the image acquisition system. After all images are corrected, theedges, surfaces and volumes and densities of the objects of interest arecalculated, i.e., the boundary that determines which pixels or voxelsbelong to the object or structure of interest and which belong to thebackground.

[0014] The computer system then calculates a set of 1D, 2D, 3D and 3D+feature values for each object or structure. For some featurecalculations the boundary along the highest gradient value is correctedby either dilating or eroding the edge (or surface) by one or morepixels (or voxels). This is done such that each feature achieves agreater discriminating power between classes of objects and is thusobject specific. These feature values are then analyzed by a classifierthat uses the feature values to determine whether the object is anartifact or is a cell nucleus or structure of interest. If the objectappears to be a cell nucleus or structure of interest, then the featurevalues are further analyzed by the classifier to determine whether theobject exhibits NDFs and CDFs indicative of disease. Based on the numberof objects found in the sample that appear to have significant NDF andCDF disease related changes, a statistical determination can be madewhether the patient from whom the cell sample was obtained is healthy orharbors a malignant growth.

[0015] In other embodiments, the present invention provides a method fordetecting epithelial cells in a cell sample and a method for detectingcells having NDFs and CDFs that are correlated with disease among theepithelial cells. In another embodiment, a method for predicting whethera patient will develop cancer is provided.

BRIEF DESCRIPTION OF THE DRAWINGS

[0016]FIG. 1 schematically shows an example illustration of a flowcytometry system as contemplated by an embodiment of the presentinvention.

[0017]FIG. 2 schematically shows an example illustration of a flowprocess for a single cell as contemplated by an embodiment of thepresent invention.

[0018]FIG. 3A and FIG. 3B schematically show an example of a cell and across-section of reconstruction as contemplated by an embodiment of thepresent invention.

[0019]FIG. 4 schematically shows an example of a reconstruction cylinderas contemplated by an embodiment of the present invention.

[0020]FIG. 5 schematically shows an example of a flow optical tomography(FOT) system as contemplated by an embodiment of the present invention.

[0021]FIG. 6 schematically shows an example of projection rays within areconstruction cylinder as contemplated by an embodiment of the presentinvention.

[0022]FIG. 7 schematically shows an example of a top view of areconstruction cylinder as contemplated by an embodiment of the presentinvention.

[0023]FIG. 8 schematically shows an example of the geometry of asource/linear array configuration used in cell reconstruction ascontemplated by an embodiment of the present invention.

[0024]FIG. 9 schematically shows an example of a classification methodused in the analysis of NDFs and CDFs from projection images andtomographic reconstruction as contemplated by an embodiment of thepresent invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] The invention is described herein with respect to specificexamples relating to biological cells, however, it will be understoodthat these examples are for the purpose of illustrating the principalsof the invention, and that the invention is not so limited. In oneexample, constructing a three-dimensional distribution of pointdensities and emission intensities within a microscopic volume allowsthe measurement of density and fluorescence at any location within themicroscopic volume and determines the location of structures, moleculesor molecular probes of interest. By using tagged molecular probes, thequantity of probes that attach to specific structures in the microscopicobject may be measured. For illustrative purposes, an object such as abiological cell may be labeled with at least one tagged molecular probe,and the measured amount and location of this probe may yield importantinformation about the disease state of the cell, including, but notlimited to, various cancers such as lung, breast, prostate, cervical andovarian cancers.

[0026] Biological cells that have been prepared for flow cytometry andstained or labeled with tagged molecular probes for specific diseasediagnosis, are caused to flow through a device that provides for thecollection of projection images and allows the reconstruction of 2D and3D density information from optical projection ray paths approximatelyperpendicular to the flow vector of the cell. By controlling thevelocity of the cell flowing along an axis, the perpendicular 2D planesof reconstruction can be correctly located (or stacked) along the axisof the cell to create a 3D picture of the entire cell, or a 3D image ofthe cell may be computed directly from 2D optical transmission oremission projections.

[0027] Flow cytometry is highly suitable for image reconstructionbecause of the following characteristics:

[0028] cells flow in single file through the capillary tube so celloverlapping and obscuration are minimized,

[0029] the velocity of cells flowing through the capillary tube can bedirectly measured and is constant,

[0030] cells tend to follow the center axis of the capillary tube andbecome structurally radially symmetric, and depending on the cellfixation and suspension preparation, cells can retain plasticity andassume an elongated shape along the z-axis in response to the velocitygradient in the capillary tube.

[0031] The present invention takes advantage of the aforesaidcharacteristics to provide a system for point source projection imagingand tomographic image reconstruction.

[0032] Referring now to FIG. 1, there shown schematically is an exampleillustration of a flow cytometry system as contemplated by an embodimentof the present invention. The system is oriented with reference to acoordinate system 11 having coordinates in the x, y and z-directions. Inoperation, cells 1 are injected into injection tube 3 using a knowninjection device 4. The capillary tube is wider at an injection end 5and includes a pressure cap 6. A sheath fluid 7 is introduced at tube 8to create laminar flow within the capillary tube 2. It is characteristicof traditional flow cytometry that the cells 1, prepared and suspendedin solution, can be forced through a capillary tube 2 such that theyelongate with the axis of flow and move approximately down the centralaxis of the capillary tube 2, as represented by dashed line 9. Cells mayadvantageously be made to flow with axial symmetry and with constantvelocity 10 in single file along the center axis of a cylindricalcapillary tube.

[0033] Referring now to FIG. 2, there shown schematically is an exampleillustration of a flow process for a single cell as contemplated by anembodiment of the present invention. A cell 1 moves with a constantvelocity (V) indicated by velocity vector 10 through a capillary tube 2.The cell 1 comprises a wall of cell cytoplasm 12 and a wall of cellnucleus 13. During the course of flowing through the capillary tube 2,the cell 1 passes through a plurality of reconstruction planesrepresented for illustrative purposes as first, second and thirdreconstruction planes 14 a, 14 b and 14 c respectively. A first planarslice 15 a through the wall of the cell cytoplasm lies withinreconstruction plane 14 a. Similarly, a second planar slice 15 b throughthe walls of the cell cytoplasm and the cell nucleus, lies within thesecond reconstruction plane 14 b, and a third planar slice 15 c lieswithin the third reconstruction plane 14 c. A central feature of thepresent invention is that a number of optical point sources ofselectable wavelength are disposed about and concentric with thecapillary tube. The optical point sources operate in conjunction withopposing optical sensor arrays that are sensitive to selectable portionsof the light spectrum, thus allowing the acquisition of projections ofthe light transmitted through the cell. The acquired projection imagesmay be analyzed directly or processed using tomographic imagereconstruction algorithms to provide spatial maps or images of thedistribution of densities and/or emission intensities within the cell.It will be understood that in practice the number of reconstructionplanes may vary from several to several hundred or more, depending onthe needed image resolution of the object being presented to the system.The group of reconstructed parallel planar slice images may be combinedor stacked in software to produce a three-dimensional (3D) image of thedensities and emission intensities within the cell. In addition, byemploying planar (2D) instead of linear (1D) light sensor arrays, and acone instead of a fan illumination ray pattern, projections of aplurality of contiguous planar slices through the flowing cells may beacquired simultaneously. As a result, three-dimensional (3D) images ofthe distribution of densities and emission intensities within the cellvolume can be computed directly from the two dimensional (2D)projections using cone-beam reconstruction algorithms. Alternatively,the 2D point source projection images, which possess infinite depth offield, can be analyzed directly.

[0034] In the case of a biological cell, a distance (d) betweenreconstruction planes may be a few microns or less. A point within thecell 1 will coincide with each reconstruction plane at time intervals,where the time intervals (t) may be described according to therelationship:

t=d÷V  (Equation 1).

[0035] Referring now jointly to FIG. 3A and FIG. 3B, there shownschematically is an example of a cross-section 16 of reconstruction ascontemplated by an embodiment of the present invention. Reconstructionof point densities within a cell from optical projections through itbenefits from the axial symmetry and centrality of the flowing cell.Additionally, the space being sampled by projections can be modeled asconsisting of three discrete compartments:

[0036] 1. the fluid outside the cell 17 (i.e. the sheath fluid or cellsuspension medium),

[0037] 2. the cell cytoplasm 18, and

[0038] 3. the cell nucleus 19.

[0039] Knowing quantitatively the distribution of optical density ormolecular probes in these three compartments is sufficient to addressmany important problems in cell biology and disease diagnosis.Additionally, it may be useful to compute two boundary surfacesincluding the cytoplasmic or cell wall 12 and the nuclear wall 13 ifcertain molecular probes bind preferentially to those surfaces.Otherwise, it may be sufficient to characterize these walls as thetransition surfaces between the three different compartments.

[0040] By combining the reconstructed slices of the cell, or byreconstructing in a helical, volumetric manner, the 3D morphology andvolume information can be generated, but absolute (as opposed torelative) volumes depend on accurate information of cell location. Celllocation is a function of the flow velocity. However, in severalinstances, the relative concentrations of density or molecular probesare sufficient to address the diagnostic question: How much probe is inthe cytoplasm relative to the nucleus vs. the non-bound probe in thebackground fluid? Or, is the probe located primarily on the cellmembrane or nuclear membrane surface?

[0041] While the cells passing through the capillary tube may becomestructurally radially symmetric, the distribution within the nuclear andcytoplasmic compartments of at least one bound molecular probe may notpossess such axial symmetry. It is therefore desirable for the imagingsystem and (particularly the emission) reconstruction algorithm toprovide sufficient spatial resolution to localize volumes of bound,fluorescing molecular probe on a scale finer than that required toprovide the three compartment analysis described above. It is furtherdesirable that the system provides quantitative information about theconcentration of asymmetrically distributed probe within the twointracellular compartments. The association of the probe with specificsubcellular compartments, structures or organelles within the cytoplasmor nucleus is facilitated by submicron spatial resolution.

[0042] A more specific example pertains to the early detection of cancerin patients at risk. In such cases, certain genes may over or underexpress their function when the cell is undergoing transformation. It isdiagnostically important to measure the relative over or underexpression of the gene product (often a protein) in the cytoplasmrelative to the nucleus, while normalizing for non-bound probes in thebackground suspension fluid. If the gene product is a protein, then atagged antibody probe may be used to assess the disease state of thecell by localizing and/or quantifying the gene product protein. Hence,the three compartment analysis can be sufficient to make this thedetermination of disease state.

[0043] Referring now to FIG. 4, there shown schematically is an exampleof a reconstruction cylinder, surrounding flow tube 2 containing flowingcells 1, as contemplated by an embodiment of the present invention. Areconstruction cylinder 20 includes, for example, a helix 24 of pointsources 21 disposed at a predetermined helical pitch, with pitch angleθ. Each point source 21 generates a beam of photons 22, where the beamof photons 22 is typically cone or fan shaped. While the arrangement ofthe sources depicted in the example of FIG. 4 is helical, the array ofpoint sources may take on a wide variety of geometric patterns,depending in part on the speed of the electronics, the cell velocity andthe geometry that achieves non-overlapping projection signals at thesensor (detector). Sensing elements 23 are disposed to receive lightfrom the point sources.

[0044] The fixed optical point sources 21, in conjunction with opposingdetectors 23 mounted around a circumference of the tube can samplemultiple projection angles through the entire cell 1 as it flows pastthe sources. By timing of the emission or readout, or both, of the lightsource and attenuated transmitted and/or scattered and/or emitted light,each detected signal will coincide with a specific, known position alongthe axis in the z-direction of the flowing cell. In this manner, a cell1 flowing with known velocity along a known axis perpendicular to alight source that is caused to emit or be detected in a synchronizedfashion, can be optically sectioned with projections through the cellthat can be reconstructed to form a 2D slice in the x-y plane. Bystacking or mathematically combining sequential slices, a 3D picture ofthe cell will emerge. It is also possible to combine the cell motionwith the positioning of the light source (or sources) around the flowaxis to generate data that can be reconstructed, for example, in ahelical manner to create a 3D picture of the cell. Reconstruction can bedone either by stacking contiguous planar images reconstructed fromlinear (1D) projections using fan-beam reconstruction algorithms, orfrom planar (2D) projections directly using cone-beam reconstructionalgorithms. The 3D picture of the cell can yield quantitative measuresof sub-cellular structures and the location and amount of taggedmolecular probes that provide diagnostic information.

[0045] As mentioned, more than one projection through a cell section isrequired to reconstruct the 2D or 3D density structure in the section orvolume. In traditional slice-by-slice medical x-ray computed tomography,multiple projections are made by holding the human patient motionlesswhile the x-ray source and opposing detectors move along a circumferenceto generate multiple projection angles through the patient. By analogy,the flow optical tomography system of this invention moves the cell at apredetermined velocity (V) past multiple sources positioned at differentangles along the circumference of the capillary tube to generatemultiple projections through the cell as it flows past the pointsources. These point sources emit photons that pass through the cell andare detected by an array of sensors opposite the source. The pointsources can be arranged along a helix 24, or in other appropriategeometric patterns, on the circumference of the capillary tube such thateach point in the cell is sampled from a multitude of angles as itpasses through the array of point sources. For good sampling geometry,these point sources may cover at least 180 degrees of circumference.Less angular coverage (i.e. angular under sampling) may be feasible insome instances, while additional radial coverage will improve accuracyand the signal-to-noise ratio of the computed reconstruction. Dependingon the geometry, it may be advantageous to apply traditional analytical,iterative or statistical algorithms for cone-beam or fan-beam imagereconstruction. (See, for example, Gilbert, P, “Iterative Methods forthe Three-dimensional Reconstruction of an Object from Projections,”Journal of Theoretical Biology 36:105-17, 1972, Oppenheim, BE, “MoreAccurate Algorithms for Iterative 3 dimensional Reconstruction,” EEETransactions on Nuclear Science NS-21:72-7, 1974, Singer, J R, Grunbaum,F A, Kohn, P, and Zubelli, J P, “Image Reconstruction of the Interior ofBodies that Diffuse Radiation,” Science 248(4958):990-3, 1990. Mueller,K and Yage, R, “Rapid 3-D Cone-beam Reconstruction with the SimultaneousAlgebraic Reconstruction Technique (SART) Using 2-D Texture MappingHardware”, IEEE Transactions on Medical imaging 19(12):1227-37, 2001.)Related methods include, but are not limited to ART (algebraicreconstruction technique, as discussed in Bellman, S H, Bender, R,Gordon, R, and Rowe, J E, “ART is Science being A Defense of AlgebraicReconstruction Techniques for Three-dimensional Electron Microscopy,”Journal of Theoretical Biology 32:205-16, 1971), SIRT (simultaneousiterative reconstruction technique, as discussed, for example byGilbert, id. #1493), ML-EM (maximum likelihood expectation maximization,as discussed, for example, by Manglos, S H, Jaszcak, R J, and Floyd, CE, “Maximum Likelihood Reconstruction for Cone Beam SPECT: Developmentand Initial Tests,” Physics in Medicine and Biology 34(12):1947-57,1989,#1382), and OSEM (ordered subsets expectation maximization, asdiscussed, for example, in Manglos, S H, Gagne, G M, Krol A, Thomas, FD, and Narayanaswamy, R, “Transmission Maximum-likelihood Reconstructionwith Ordered Subsets for Cone Beam CT”, Physics in Medicine and Biology40(7):1225-41, 1995, #4389).

[0046] Methods

[0047] Flow Cytometer

[0048] Referring now to FIG. 5, there shown schematically is an exampleof a flow optical tomography system (FOT) as contemplated by anembodiment of the present invention. The flow optical tomography systemincludes a flow cytometer, with a reconstruction cylinder 20 positionedaround capillary tube 2. A source of photons 25 and a photon sensor 26work together with pulse height analyzer 27 to operate as a triggeringdevice. Pulse height analyzer 27 operates in accordance with knownprincipals to provide a first trigger point 28 for the beginning of acell, and a second trigger point 29 for the end of the cell. The pulseheight analyzer 27 outputs a trigger signal 30 corresponding to thebeginning and end of each cell, where the trigger signal is received bythe reconstruction cylinder 20.

[0049] A computer 40 is coupled to transmit data, control signals andtiming signals to the point sources 21, sensing elements 23 and pulseheight analyzer 27 by signal lines 41-43. The computer may comprise aknown computer or plurality of computers and array processors adequatefor image acquisition and image reconstruction processing.

[0050] Commercial flow cytometers come with three basic flowconfigurations: namely, the cylindrical flow tube, the rectangular flowtube and the flow in air system. (See Shapiro, H M, Practical FlowCytometry 3^(rd) ed., Wiley-Liss, 1995.) The preferred configuration isthe cylindrical flow tube, because it is important to preserve optimalcylindrical geometry for the reconstruction algorithm to minimize anyradial dependence due to the flow hardware (see FIG. 1). Moreover, thecylindrical flow tube may have uniformly thin walls relative to thecross-sectional area of the capillary tube.

[0051] Additionally, the triggering device may be located upstream fromthe reconstruction module to provide a timing signal to initiate thenterminate data collection as the cell optimally enters then emerges fromthe reconstruction cylinder. The triggering device may advantageouslycomprise a laser diode, CCD, PMT, a photodetector combination, a solidstate photodetector and combinations of the foregoing elements. Thetriggering device has a threshold setting that senses the presence of aflowing cell, thus generating a trigger signal that, in conjunction witha known cell velocity, can be used to calculate when the downstreamreconstruction cylinder may commence data collection for that particularcell of interest. Further, the time interval between the first andsecond trigger points corresponding to the entrance and exit of the cellinto and out of the reconstruction cylinder, may be divided into equalor unequal increments during each of which additional projection datamay be acquired by strobing the light source(s) 21 and reading out thesensor array(s) 23.

[0052] The velocity of flow needs to be controlled and measuredaccurately. This capability is provided for in high-end commercialsystems that use velocities in the range of 1 meter/sec to 10meters/sec. The best cell velocity will be determined by the speed ofdata collection and signal-to-noise considerations as discussedsubsequently.

[0053] The Reconstruction Module

[0054] Referring now to FIG. 6, there shown schematically is an exampleof fan-beam projection rays within a reconstruction cylinder 20 ascontemplated by an embodiment of the present invention. The purpose ofreconstruction cylinder is to provide a means of projecting light from aplurality of fixed point sources 21 a-21 c, along a small circumference,into the cylindrical capillary tube. The photons emitted from the pointsources have a known projection geometry, such as a fan or a cone shape,and pass through the capillary tube to be detected by an array ofsensing elements 23 a, 23 b or 23 c, as the case may be, located along alarger circumference opposite a corresponding point source. While curvedlinear (1D) sensor arrays suitable for fan-beam transillumination aredepicted for purposes of illustration, it is to be understood thatstraight linear (1D) sensor arrays or planar (2D) sensor arrays suitablefor cone-beam illumination may be similarly employed. In this manner, aset of projection rays can be generated where the projection rays can bedescribed as the straight line connecting the point source to anindividual sensing element. The difference between the number of photonsleaving the point source along a particular projection ray, such as ray31, and the number of photons received at the particular sensing elementis related to the number of photons lost or attenuated due tointeractions with the cell and other contents of the flow tube along theprojection ray path.

[0055] However, complications may arise from light scatter, photonenergy shifts, imperfect geometry and poor collimation, and photons fromdifferent sources may arrive at a particular sensing element whenmultiple point sources are energized simultaneously. With carefulconstruction of the reconstruction cylinder, for example by judiciouschoice of the geometry for the pattern of point sources and theiropposing detectors as described herein, and by proper timing ormultiplexing of activation of the multiple point sources and readout ofthe sensor arrays, the photon contamination due to these issues can beminimized but not eliminated.

[0056] Photon contamination can be accounted for by calibration of thesystem, for example, with no cells present. That is, each light sourcemay be illuminated in turn and its effects on each of the sensors can bemeasured, thereby providing offset data for use in normalizing thesystem. An additional calibration step may entail, for example, imaginglatex polymer beads or other microspheres or oblate spheroids whoseoptical properties are known and span the density range of interest forcellular imaging. Photons emitted from fluorescing probes may bedifferentiated from those originating at the point sources by the use ofspectral bandpass filters at the detector as discussed subsequently.

[0057] Light Source

[0058] Each source may have the same general characteristics,preferably:

[0059] it may approximate a small circular point source,

[0060] it may be bright with known spectral content,

[0061] the photons emitted from the source may have a known geometrysuch as a cone-beam or a fan-beam.

[0062] Each source creates data for one projection angle. A plurality ofsources arranged along a helix whose axis is the center axis of the flowtube creates data from multiple projection angles through eachsuccessive plane (or reconstruction slice) as the cell flows through themodule. Depending on the sensor geometry, several point sources could bearranged co-linearly on the same circumference such that the projectionsdo not overlap at the sensor. A good sampling geometry can be achievedby placing sources equidistant along a helix of 180 degrees, though lessangular coverage may be tolerable in some instances, and 360 degrees maybe employed to improve the signal-to-noise ratio. The desired number ofsources is a function of the needed resolution within each planarreconstruction (the x-y plane) or volumetric reconstruction. While ahelical arrangement of point sources is used for illustration, it is tobe understood that a variety of geometric patterns may be employed forthe point source array. Further, the wavelength of the sources isselectable either by use of various diode or other lasers or by bandpassfiltering of a white or other broadband source, for example a mercury orxenon arc lamp.

[0063] Referring now to FIG. 7, there shown schematically is an exampleof a top view of a reconstruction cylinder, as shown in FIG. 6, ascontemplated by an embodiment of the present invention. A first pointsource 21 a and sensor array 23 a are shown with a cell with nucleus 19flowing perpendicular to the page in a projection of the sourcetrajectory which, in two dimensions, constitutes a circle ofreconstruction 32, in which all projections, even though acquired in atemporally staggered fashion, are depicted as overlapping, and in whichthe whole cell is contained. A second point source 21 b and secondsensor array 23 b are located about 30° around the helix. A third pointsource 21 c and third sensor array 23 c are located about 90° around thehelix.

[0064] Data collection is gated in synchrony with the cell velocitywithin a “thick” planar axial cross-section of the cell. The desiredplane thickness is a function of the needed resolution in thez-direction. Typically, the resolution in the axial (z-direction) willbe less than in the planar transaxial direction. Also, the best circleof reconstruction may be defined by the overlapping intersection of theprojection fans having the point sources at their apexes and the widthof the sensing arrays at their bases. It is desirable that the geometryof the reconstruction cylinder assures that the flowing cell crosssection is contained entirely within the circle of reconstruction.

[0065] There are several options that can be employed to create opticalpoint sources, such as:

[0066] a pinhole in front of a laser or other high intensity photonsource,

[0067] an optical fiber with a small cross-section,

[0068] a short focal length lens in front of a photon source,

[0069] an electron beam that irradiates a point on a phosphor surface (aform of CRT), and

[0070] various combinations of the above.

[0071] The geometry is such that the closer the point source to theobject of interest (the cell) the higher the magnification due to thewider geometric angle that is subtended by an object closer to thesource. Conversely, if the required resolution is known in advance ofthe system design, then the geometry can be optimized for thatparticular resolution. For background, those skilled in the art aredirected to Blass, M, editor-in-chief, Handbook of Optics: Fiber Opticsand Nonlinear Optics, 2^(nd) ed., Vol. IV, Mcgraw-Hill, 2001.

[0072] Referring now to FIG. 8, FIG. 8 schematically shows an example ofa straight linear array 23 used in cell reconstruction as contemplatedby an embodiment of the present invention. For example, given a cellcross section 12 and nucleus 19 contained within a 30-micron diametercircle of reconstruction and a desired resolution of 0.5 microns, thenNyquist sampling (i.e. over-sampling by a factor of two) dictates thatat least 120 sensing elements 33 are required for each point source 21.The point source 21 at the apex and the linear array length 34 at thebase form a triangle 35, such that the example 30-micron diameter cellfits within the triangle 35 as closely as possible to the point source21. In this example, if each element of the (e.g. CCD) array is 20microns wide and the array length is 2400 microns, then the center ofthe cell may be located about 100 microns (half the diameter of thecapillary tube) from the point source when the distance between thepoint source and the linear array is 8 millimeters, providing 80-foldmagnification.

[0073] As a second example, consider a 30-micron diameter circle ofreconstruction and a complementary metal oxide semiconductor (CMOS)sensing array in which the pixel element size is 4 microns. In this casethe array may contain 120 elements for a width of 480 microns, and itmay be positioned 1.6 mm from the point source when the distance betweenthe point source and the cell is 100 microns, providing a 16-foldmagnification.

[0074] Sensing Elements

[0075] Each point source shall have a corresponding array of sensingelements such as CCDs in some straight or curved geometric arrangementopposite the point source to receive photon rays transmitted through thecircle of reconstruction. Typically, the linear array may have its arrayof sensing elements centered on the line between the point source andthe central flow axis, and may line up perpendicularly to the flow axis.It is possible to use 2D arrays where only a subset of each line ofelements within the 2D array is read out for the reconstruction input.In a 2D array, each successive subset of elements may be staggered bythe appropriate number of elements to align properly with each differentpoint source along the helical arrangement of point sources.

[0076] For slice-by-slice fan-beam reconstruction using the example ofthe 30-micron reconstruction circle with 120 sensing elements per fan toobtain a resolution of 0.5 microns, a 2D array with 2000×2000 20-micronelements is sufficient for sensing 136, point sources arranged at 1radial degree increments, where the average off-set between successiveviews is 300 microns, where that is equivalent to 15 sensing elements.(At the center of the array, the offset may be 140 microns, or 7elements, while at the edges of the array, a 1 radial degree sweepbetween views may entail a considerably larger offset.) If groups of 15rows of the sensor array were averaged to provide the projection datafor one slice, the z-resolution within the cell image may be 3.75microns, whereas if 2 rows were averaged for each projection, the axialresolution in object space may be 0.5 microns, equivalent to thetransaxial resolution.

[0077] In a preferred embodiment of the invention, the 2D array iscurved along a cylindrical circumference that may be concentric with thereconstruction cylinder, so that the ray paths are all equal in length.For the case of a 30-micron reconstruction circle, a curved, 2D arraythat possessed only the elements required to oppose a helical locus ofpoint sources may be a helical strip 120 elements wide by some multipleof 136 elements in height (length) for the example described above.

[0078] Though planar or “thick” fan illumination is described forpurposes of the illustration above, it is to be understood that true,uncollimated cone-beam illumination may be employed in conjunction with2D planar detectors, providing for 3D reconstruction using cone-beamalgorithms. The 2D projection images may be analyzed directly to obtain,for example, information about the disease status or transformationstate of the cell. For direct, volumetric cone-beam reconstruction, theillumination from a plurality of point sources is multiplexed in such away that, given the geometric arrangement of the point sources anddetectors, the cones of illumination from different point sources do notoverlap on the sensor array.

[0079] Moreover, if the cell were flowing with a velocity of 1 meter/sec(or 1,000,000 microns/sec) and each element in the 2D array were 20microns wide, then a line read-out every 20 microseconds may readilycapture data within a 0.25-micron slice of the cell. For the case when15 sensor rows are averaged to provide the data for a 3.75-micron slice,readouts would have to occur every 300 microseconds. A significantimprovement in reconstruction image quality is achieved using a larger2D array.

[0080] One embodiment of the present invention especially suited formultispectral imaging of a number of transmitted or emitted wavelengthbands may advantageously include two or more reconstruction modulesarranged in series. Such multiple reconstruction cylinders may beseparated by intervening sections of capillary tube, and each moduleprovides projection data adequate to produce a complete reconstructedimage of the objects flowing through it. The point sources and/or sensorarrays for each of the several reconstruction modules may be optimizedfor a particular spectral band. For example, a first reconstructionmodule may employ intense white light illumination and unfiltereddetection to provide a complete projection data set to reconstruct a mapof object optical density, absorption or scattering coefficients, whilea second reconstruction module may employ illumination, such as anargon-ion laser (488 um), in a narrow spectral band centered around 495nm to excite fluorescing probe labeling proteins for animmunofluorescence study in conjunction with filtered sensor arrayssensitive to the 520-nm emission to provide a second complete projectiondata set sufficient to map the concentration distribution of the labeledproteins by using emission reconstruction algorithms, as describedsubsequently. Yet a third reconstruction module may use narrow-bandillumination centered around 535 and/or 342 nm to excite propidiumiodide bound stoichiometrically to DNA and filtered sensor arrays tooptimally detect the red (617-nm) emissions for studies of ploidy. Itwill be understood that the aforesaid examples are for illustrativepurposes, and that the method applies generally to any wavelengthcombinations for illuminating and sensing.

[0081] Image Reconstruction

[0082] The most common and easily implemented reconstruction algorithms,known as filtered back projection methods, are derived from a similarparadigm in computerized x-ray tomography (CT) using cone-beam andfan-beam geometry. (See the following references, for example, Kak, A Cand Slaney, M, Principles of Computerized Tomographic Imaging, IEEEPress, New York, 1988, and Hernan, G, Image Reconstruction fromProjections: The Fundamentals of Computerized Tomography, AcademicPress, New York, 1980.) These methods are based on theorems for Radontransforms with modifications that reflect the particular geometry ofthe source/detector configuration and the ray paths in the irradiatingbeam. However, in the case of clinical x-ray CT, for slice-by-sliceacquisition, the human subject is usually held motionless while thex-ray source and detector arrays may move along an arc around thepatient to collect data from multiple projection angles within a givenslice. Then the human subject is repositioned along the z-axis andanother slice of data is collected, etc. Alternatively, in the moremodern clinical helical CT, the patient may be continuously translatedin the z-direction while the source-detector assembly rotatescontinuously to provide helical projection data, which is theninterpolated to provide projections orthogonal to the patient z-axis. Inflow optical tomography, the subject (a cell) is moved with constantvelocity relative to the stationary sources and detector arrays whereinthe plurality of source/detector systems acquire data in synchrony withspecific gated time points along the cell velocity vector in a fashionthat generates multiple projection angle data within a given slice orvolume. For slice-by-slice scanning, the reconstruction algorithm willcompute a 2D image of a plane perpendicular to the axis of motion, andthe serial stacking of multiple slices will generate the 3D picture ofthe subject where contrast is a function of the variations in the x-rayattenuation coefficient or optical density within the subject for CT orflow optical tomography, respectively. For volumetric, cone-beamscanning the reconstruction algorithm computes a 3D image of a volumewithin the cell or other object directly from planar transmission oremission optical projections, where the contrast is a function of theoptical density and/or tagged probe density distribution, respectively,within the imaged object.

[0083] It may be desirable for either the transmission data to producethe cell density reconstruction or for the emission data to reconstructthe labeled probe distribution, or both, to employ image reconstructionalgorithms other than filtered back projection. The general class knownas iterative reconstruction algorithms is more efficacious in someinstances, especially for emission tomography or when it is possible, asin the instance of the current invention where the axial symmetry andtricompartmental nature of the object are known, to incorporate a prioriinformation into the reconstruction algorithm to improve the quality ofthe reconstruction(See, for example, Gilbert, P, “Iterative Methods forthe Three-dimensional Reconstruction of an Object from Projections,”Journal of Theoretical Biology 36:105-17, 1972, and other referencesnoted hereinabove).

[0084] Similarly, one method may advantageously use finite elementmodeling-based (FEM), statistical reconstruction algorithms. FEMalgorithms are derived from linear transport theory in which the photondiffusion/migration equation is solved at all the boundaries of theelements to produce a two- or three-dimensional map of some combinationof the absorption, scattering, refractive index and anisotropy factorproperties of the imaged object. Examples of such methods are taught inPaulsen, K D and Jiang, H, “Spatially Varying Optical PropertyReconstruction Using a Finite Element Diffusion Equation Approximation”,Medical Physics 22(691-701) 1995, Hampel, U and Freyer, R, “Fast ImageReconstruction for Optical Absorption Tomography in Media with RadiallySymmetric Boundaries”, Medical Physics 25 (1):92-101, 1998, and Jiang,H, Paulsen, K D, and Osterberg, U L, “Frequency-domain Near-infraredPhoto Diffusion Imaging: Initial Evaluation in Multitarget TissuelikePhantoms”, Medical Physics 25(2): 183-93,1998.

[0085] Chromatic Separation

[0086] If a polychromatic point source is used (e.g., white light) thendifferent chromatic stains (e.g. chromaphors) can be utilized todistinguish a number of molecular probes and structural features withina given cell. Here, serial bandpass filters at the source or sensingarray (or both) separate the wavelength data and allow thereconstruction and spatial localization of the individually stainedmolecules. A more robust method for imaging multiple probes involves anintense white light source and simultaneous collection of multiplefiltered bandwidths in the sensor arrays thus allowing the imagereconstruction algorithms to compute spatial image slices for eachchromaphor. These may be displayed as colored images.

[0087] Fluorescence, Phosphorescence, Chemiluminescence andNano-Particle Emission As a special case of the flow optical tomographysystem, certain molecular probes can be tagged with a “reporter” thatemits light of a different (longer) wavelength when stimulated by theprimary source of photons. The secondary emission from the reporter canbe filtered with standard optical filters to separate the primary sourcephotons from the secondary emission photons. However, the secondaryemission photon image reconstruction algorithm is further complicated,because the secondary photons do not necessarily arise in a direct raypath from the point source. If it is assumed that the secondary photonsradiate from the secondary point source in a uniform spherical pattern,then the intensity of the secondary photons reaching any sensing elementwill be a simple function of distance to the sensing element. A furtherrefinement may account for the non-spherical distribution of photonsfrom the secondary source by providing a model of the spatialdistribution of secondary photons relative to the point source andwithin the reconstruction slice. Either method will provide a means tocalculate the location of the secondary source in the reconstructionslice or volume. Collimation between the imaged object and the detectorarray(s) will improve the image reconstruction.

[0088] If the primary photon intensities and the secondary photonintensities are measured simultaneously by optical filtration, the highresolution density reconstruction from the primary photon intensitiescan be superimposed upon or fused with the secondary sourcereconstruction such that image morphology along with localized probeconcentration is available in a single reconstructed image. Depending onintensity, signal-to-noise and on the ability to filter or producenarrow bandwidths of photons at the source and/or sensors, it may beadvantageous to utilize multiple secondary sources each corresponding toa different tagged molecular probe.

[0089] U.S. Pat. No. 6,201,628 entitled “High Throughput OpticalScanner”, issued Mar. 13, 2001, to Basiji, et a/. discloses a scanningapparatus provided to obtain automated, rapid and sensitive scanning ofsubstrate fluorescence, optical density or phosphorescence. The scanneruses a constant path length optical train, which enables the combinationof a moving beam for high speed scanning with phase-sensitive detectionfor noise reduction, comprising a light source, a scanning mirror toreceive light from the light source and sweep it across a steeringmirror, a steering mirror to receive light from the scanning mirror andreflect it to the substrate, whereby it is swept across the substratealong a scan arc, and a photodetector to receive emitted or scatteredlight from the substrate, wherein the optical path length from the lightsource to the photodetector is substantially constant throughout thesweep across the substrate. The optical train can further include awaveguide or mirror to collect emitted or scattered light from thesubstrate and direct it to the photodetector. For phase-sensitivedetection the light source is intensity modulated and the detector isconnected to phase-sensitive detection electronics. A scanner using asubstrate translator is also provided. For two dimensional imaging thesubstrate is translated in one dimension while the scanning mirror scansthe beam in a second dimension. For a high throughput scanner, stacks ofsubstrates are loaded onto a conveyor belt from a tray feeder. U.S. Pat.No. 6,201,628 is incorporated herein by reference. However, neither ofthese patents allows for the generation of optical projection images,and consequently, tomographic reconstruction is not possible withoutsuch images.

[0090] As described above, the present invention is a system forautomatically detecting NDFs in the nuclei and CDFs in the cytoplasmsfrom optical projections and tomographic reconstructions of cellsobtained from a patient. From the presence or absence of disease relatedNDFs and CDFs, a determination can be made whether the patient has amalignant cancer.

[0091] Referring now to FIG. 9, there shown schematically is an exampleof a classification method with cell reconstruction data as contemplatedby an embodiment of the present invention. The system may includeseveral classifiers that can work together to determine whether aparticular cell sample contains cells of interest and diagnostic cellshaving NDFs and CDFs associated with disease. A classifier is a computerprogram that analyzes an object based on certain feature values 94. Theautomated classifier system of the present invention may include, forexample, a primary classifier 90, which performs a basic screeningfunction, and selects objects of interest. A secondary classifier 92classifies cellular objects as morphologically abnormal ormorphologically normal and having NDFs and CDFs associated with disease,or normal and not exhibiting disease related NDFs and CDFs. A report 93may be provided detailing the classification results of any one or allof the classifiers. It will be understood that many and variousclassifiers may be employed. Depending upon the application.

[0092] As noted above, while the automated system of the presentinvention may include a primary and secondary classifier, a singleclassifier can be used to sequentially obtain the classificationsachieved by the present invention. The software packages used togenerate classification functions based on statistical methods aregenerally commercially available.

[0093] The automated classifier of this invention preferably includesclassifiers that utilize fuzzy binary decisions or Bayesian statisticaldecisions based on directly calculated NDFs and CDFs in performance oftheir classification function. The classifier can be constructed toinclude a large number of feature values, for example, including themorphological features, photometric features, discrete texture features,Markovian texture features, non-Markovian texture features, fractaltexture features and run length texture features.

[0094] The primary classifier may typically function to subtype objectsof interest into three classes: (1) epithelial cells includingdiagnostic cells and cells that may contain NDFs and CDFs associatedwith disease; (2) inflammatory cells; and (3) artifacts. The primaryclassifier can affect cell-by-cell classification through a binarydecision tree incorporating a selection of feature values.

[0095] As indicated above, the ability of the system of the presentinvention to distinguish cell nuclei from artifacts, epithelial cellsfrom other cell types, and cells having NDFs and CDFs associated withdisease from other normal epithelial cells depends on the ability of theclassifier to make distinctions based on the values of the featurescomputed. For example, to distinguish normal epithelial cells fromabnormal epithelial cells (i.e., diagnostic cells), the presentinvention may apply several different discriminant functions, each ofwhich is trained to identify particular types of objects and particularchanges in NDFs and CDFs.

[0096] The secondary classifier classifies the epithelial cells in thecell sample selected by the primary classifier and also uses a binarydecision tree and feature values in performance of its classificationfunction. The secondary classifier, which can be considered as asample-by-sample classifier, analyzes the epithelial cells classified bythe primary classifier and classifies those cells as normal and diseaseNDF or CDF-negative or normal and disease NDF or CDF-positive. As withthe primary classifier, the secondary classifier is constructed todistinguish cells based on a set of preferred NDF and CDF features.

[0097] The feature sets used by each classifier are developed fromdiscriminant functions analyzing the quantitative density features ofcell nuclei and/or cytoplasm and, preferably, include a minimum numberof features. Ideally, the selection of a minimum number of optimalnuclear features results in an efficient and robust classifier. That is,a classifier is preferably both efficient in accurately classifying acell or a cell type, and robust in reliably classifying a variety ofcell and sample preparations.

[0098] The classifier may include at least one discriminant function,wherein the discriminant function uses features from shadowgrams toclassify cells of interest in the sample including, but not be limitedto, 1D and 2D nuclear densitometric features (NDFs) and cytoplasmicdensitometric features (CDFs) and features selected from the groupconsisting of area, mean radius, optical density (OD) variance, ODskewness, OD range, OD average, OD maximum, density of light spots, lowDNA area, high DNA area, low DNA amount, high DNA amount, high averagedistance, mid/high average distance, correlation, homogeneity, entropy,fractal dimension, DNA index, texture, punctateness, connectedcomponents and the various harmonics in spatial density frequency space.

[0099] The cell sample may be a human lung specimen, a human cervicalspecimen, a blood specimen, or may contain rare cells. In one example,the staining of a sample to identify NDFs and CDFs comprises stainingwith a stoichiometric or proportional DNA and RNA stain. The DNA stainmay be selected from the group consisting of Feulgen stain, Romanowskistain, May-Grunwald-Giemsa stain, Methyl Green and thionin. 9. Inanother example, the sample may be stained with an antibody marker toidentify NDFs and CDFs. In another example, a nucleic acid sequencemarker may advantageously be used to stain the sample.

[0100] The system of the invention is useful for analyzing various typesof biological cells. In one example, the cell of interest can beselected to be diagnostic of cancer, and/or, the cell of interest mayadvantageously be a preinvasive cancer cell. The cell of interest maycomprise an invasive cancer cell where the cells of interest areutilized in screening a patient for cancer. The cells of interest mayalso be utilized in determining whether a patient will develop invasivecancer. The invasive cancer cell can be derived from an epithelialcancer. Alternatively, or in addition, the preinvasive cancer cell canbe derived from an epithelial cancer. The epithelial cancer may beadvantageously selected from the group consisting of lung and throatcancer, cervical and ovarian cancer, breast cancer, prostate cancer,skin cancer and cancer of the gastrointestinal tract.

[0101] In an embodiment used to analyze multi-dimensional images, suchas, for example, 3D images and higher-dimensional (3D+) images, thefeatures used to classify cells of interest in the sample may comprise,but are not limited to, 1D, 2D, 3D and 3D+ nuclear densitometricfeatures (NDFs) and cytoplasmic densitometric features (CDFs) andfeatures selected from the group consisting of area, mean radius,volume, mean volume, optical density (OD) variance, OD skewness, ODrange, OD average, OD maximum, density of light spots, low DNA volume,high DNA volume, low DNA amount, high DNA amount, high average distance,mid/high average distance, correlation, homogeneity, entropy, fractaldimension, DNA index, texture, punctateness, connected components andthe various harmonics in spatial density frequency space. High DNAvolume, for example, may be that which is above a baseline as measuredin a population of normal cells.

[0102] The invention has been described herein in considerable detail inorder to comply with the Patent Statutes and to provide those skilled inthe art with the information needed to apply the novel principles of thepresent invention, and to construct and use such exemplary andspecialized components as are required. However, it is to be understoodthat the invention may be carried out by specifically differentequipment, and devices and reconstruction algorithms, and that variousmodifications, both as to the equipment details and operatingprocedures, may be accomplished without departing from the true spiritand scope of the present invention.

What is claimed is:
 1. A method for detecting cells of interest in acell sample, comprising the steps of: (a) obtaining a cell sample andsuspending the cells in solution; (b) if required, fixing the cells ofthe cell sample in solution; (c) marking the cells to generate opticaldensities within each cell of the sample; (d) illuminating the samplewith at least one point source of light; (e) obtaining at least oneprojection image through the sample with a digital array detector; (f)compensating the at least one projection image for variations inbackground illumination; (g) analyzing the at least one projection imageto detect at least one object of interest; (h) calculating a set offeature values having one-dimensional (1D) feature values andtwo-dimensional (2D) feature values for the at least one object ofinterest; (i) providing the set of feature values to at least oneclassifier; and (j) using the set of feature values and the at least oneclassifier for identifying the at least one object of interest from thecell sample.
 2. The method of claim 1, wherein the set of feature valuesused to classify the at least one object of interest in the sample areselected from the group consisting of 1D and 2D nuclear densitometricfeatures (NDFs), cytoplasmic densitometric features (CDFs), area, meanradius, optical density (OD) variance, OD skewness, OD range, ODaverage, OD maximum, density of light spots, low DNA area, high DNAarea, low DNA amount, high DNA amount, high average distance, mid/highaverage distance, correlation, homogeneity, entropy, fractal dimension,DNA index, texture, punctateness, connected components and harmonics inspatial density frequency space.
 3. The method of claim 1, wherein thestep of using the at least one classifier comprises using a classifierhaving at least one discriminant function.
 4. The method of claim 3wherein the at least one discriminant function uses a set ofdiscriminant features selected from the group consisting of 1D and 2Dnuclear densitometric features (NDFs), cytoplasmic densitometricfeatures (CDFs), area, mean radius, optical density (OD) variance, ODskewness, OD range, OD average, OD maximum, density of light spots, lowDNA area, high DNA area, low DNA amount, high DNA amount, high averagedistance, mid/high average distance, correlation, homogeneity, entropy,fractal dimension, DNA index, texture, punctateness, connectedcomponents and harmonics in spatial density frequency space.
 5. Themethod of claim 1 wherein the cell sample comprises a human lungspecimen.
 6. The method of claim 1 wherein the cell sample comprises ahuman cervical specimen.
 7. The method of claim 1 wherein the cellsample comprises a human blood sample.
 8. The method of claim 1 whereinthe cell sample comprises rare cells.
 9. The method of claim 1 whereinthe step of marking the sample comprises staining with a stain selectedfrom the group consisting of stoichiometric DNA stain, stoichiometricRNA stain, proportional DNA stain, and proportional RNA stain.
 10. Themethod of claim 9 wherein the step of marking the sample comprisesstaining with a stain selected from the group consisting of Feulgenstain, Romanowski stain, May-Grunwald-Giemsa stain, Methyl Green andthionin.
 11. The method of claim 1 wherein the step of marking thesample comprises staining with an antibody marker.
 12. The method ofclaim 1 wherein the step of marking the sample comprises staining with anucleic acid sequence marker.
 13. The method of claim 1 furthercomprising the step of selecting the at least one object of interest toinclude cancer diagnostics.
 14. The method of claim 1 further comprisingthe step of selecting the at least one object of interest to include apreinvasive cancer cell.
 15. The method of claim 1 further comprisingthe step of selecting the at least one object of interest to include aninvasive cancer cell.
 16. The method of claim 1 further comprising thestep of using the at least one object of interest for screening apatient for cancer.
 17. The method of claim 1 further comprising thestep of using the at least one object of interest for determiningwhether a patient will develop invasive cancer.
 18. The method of claim14 wherein the preinvasive cancer cell is derived from an epithelialcancer.
 19. The method of claim 18 wherein the epithelial cancer isselected from the group consisting of lung cancer, throat cancer,cervical cancer, ovarian cancer, breast cancer, prostate cancer, skincancer and cancer of the gastrointestinal tract.
 20. The method of claim15 wherein the invasive cancer cell is derived from an epithelialcancer.
 21. The method of claim 20 wherein the epithelial cancer isselected from the group consisting of lung cancer, throat cancer,cervical cancer, ovarian cancer, breast cancer, prostate cancer, skincancer, cancer of the gastrointestinal tract, lymphatic cancer and bonecancer.
 22. The method of claim 14 wherein the preinvasive cancer cellis derived from a neuroendocrine cancer.
 23. The method of claim 22wherein the neuroendocrine cancer is selected from the group consistingof lung and throat cancer, cervical cancer, breast cancer, and cancer ofthe gastrointestinal tract, lymphatic cancer and bone cancer.
 24. Themethod of claim 15 wherein the invasive cancer cell is derived from aneuroendocrine cancer.
 25. The method of claim 24 wherein theneuroendocrine cancer is selected from the group consisting of lungcancer, throat cancer, cervical cancer, breast cancer, and cancer of thegastrointestinal tract, lymphatic cancer and bone cancer.
 26. The methodof claim 1 further including the step of combining at least twoprojection images using methods for computerized image reconstruction togenerate multi-dimensional images.
 27. The method of claim 26 whereinthe computerized image reconstruction methods include methods selectedfrom the group consisting of fan beam projection geometries and conebeam projection geometries.
 28. The method of claim 26 furthercomprising steps wherein the multi-dimensional images, are processed asfollows: (a) compensating the multi-dimensional images for variations inbackground illumination; (b) analyzing the multi-dimensional images todetect at least one object of interest; (c) calculating a surface thatbounds each object of interest; (d) calculating a set ofmulti-dimensional feature values for each object of interest; and (e)providing the set of multi-dimensional feature values to at least oneclassifier that identifies and characterizes cells of interest in thecell sample.
 29. The method of claim 28 wherein the multi-dimensionalfeature values used to classify cells of interest in the sample areselected from the group consisting of 1D nuclear densitometric features(NDFs), 2D NDFs, 3D NDFs and 3D+ NDFs, 1D cytoplasmic densitometricfeatures (CDFs), 2D CDFs, 3D CDFs and 3D+ CDFs, area, mean radius,volume, mean volume, optical density (OD) variance, OD skewness, ODrange, OD average, OD maximum, density of light spots, low DNA volume,high DNA volume, low DNA amount, high DNA amount, high average distance,mid/high average distance, correlation, homogeneity, entropy, fractaldimension, DNA index, texture, punctateness, connected components andharmonics in spatial density frequency space.
 30. The method of claim 28wherein the multi-dimensional feature values include 1D, 2D, 3D and 3D+features that are utilized by a classification program to characterizecells of interest with regard to type, maturation, disease status,relative abundance and presence of quantitative markers.
 31. The methodof claim 28 wherein the classifier detects and characterizes cells ofinterest including rare cells.
 32. The method of claim 28 furtherincluding the step of diagnosing the cell sample based upon thecharacterization of cells.
 33. A method for identifying the at least oneobject of interest from a cell sample, comprising the steps of: (a)obtaining a cell sample and suspending the cells in solution; (b) ifrequired, fixing the cells of the cell sample in solution; (c) markingthe cells to generate optical densities within each cell of the sample;(d) illuminating the sample with at least one point source of light; (e)obtaining at least one projection image through the sample with adigital array detector; (f) compensating the at least one projectionimage for variations in background illumination; (g) analyzing the atleast one projection image to detect at least one object of interest;(h) calculating a set of feature values having one-dimensional (1D)feature values and two-dimensional (2D) feature values for the at leastone object of interest, wherein the set of feature values used toclassify the at least one object of interest in the sample are selectedfrom the group consisting of 1D nuclear densitometric features (NDFs),2D NDFs, 1D cytoplasmic densitometric features (CDFs), 2D CDFs, area,mean radius, optical density (OD) variance, OD skewness, OD range, ODaverage, OD maximum, density of light spots, low DNA area, high DNAarea, low DNA amount, high DNA amount, high average distance, mid/highaverage distance, correlation, homogeneity, entropy, fractal dimension,DNA index, texture, punctateness, connected components and harmonics inspatial density frequency space; (i) providing the set of feature valuesto at least one classifier; and (j) using the set of feature values andthe at least one classifier for identifying the at least one object ofinterest from the cell sample.
 34. The method of claim 1 wherein the atleast one projection image comprises a shadowgram.